Deep Learning-Enhanced Compressive Sensing for Efficient Real Time Iot Signal Reconstruction

The rapid expansion of the Internet of Things (IoT) has created massive volumes of sensor-generated data that require efficient transmission and real-time reconstruction. Traditional signal processing approaches often fall short in balancing compression efficiency, reconstruction accuracy, and low latency. Compressive Sensing (CS) has emerged as a promising technique to address these challenges, but its performance in real-world IoT environments is limited by high computational costs and reconstruction delays. To overcome these barriers, this work proposes a deep learning-assisted compressive sensing framework that integrates neural networks with classical CS methods for efficient signal recovery. The approach leverages a convolutional autoencoder to learn robust feature representations from sparse measurements, enabling faster and more accurate reconstruction of IoT signals. Experiments conducted on benchmark IoT datasets demonstrate significant improvements in both recovery accuracy and speed compared to conventional CS algorithms. The proposed framework achieves higher peak signal-to-noise ratio (PSNR) and reduced mean squared error (MSE), while also lowering reconstruction latency, making it well-suited for real-time IoT applications such as smart healthcare, environmental monitoring, and industrial automation. Thus, this study highlights the synergy between deep learning and compressive sensing, offering a scalable and practical solution to meet the growing demands of IoT signal processing.